A yearlong study of water-soluble organic carbon in Beijing I: Sources and its primary vs. secondary nature

Atmospheric Environment 92 (2014) 514e521

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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv

A yearlong study of water-soluble organic carbon in Beijing I: Sources and its primary vs. secondary nature Zhenyu Du a, Kebin He a, b, c, *, Yuan Cheng a, *, Fengkui Duan a, Yongliang Ma a, Jiumeng Liu d, e, Xiaolu Zhang d, f, Mei Zheng d, Rodney Weber d a

State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, China Collaborative Innovation Center for Regional Environmental Quality, China d School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA e Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA f Department of Civil and Environmental Engineering, University of California, Davis, CA, USA b c

h i g h l i g h t s
WSOC correlated strongly with OC and the WSOC to OC ratio peaked in summer.
WSOC exhibited strong correlation with secondary species.
The variation of the WSOC to EC ratio coincided with that of relative humidity.
Levoglucosan was introduced to the PMF model to investigate the sources of WSOC.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 18 October 2013 Received in revised form 7 March 2014 Accepted 30 April 2014 Available online 2 May 2014

Sources and properties of water-soluble organic carbon (WSOC) were investigated based on fine particulate matter (PM2.5) samples collected in Beijing during a thirteen month campaign. The WSOC to OC ratios averaged 45.9% annually and were substantially higher in summer compared with the other seasons. WSOC exhibited strong correlation with secondary components such as secondary organic aerosol estimated by the elemental carbon (EC)-tracer method and inorganic ions (e.g., sulfate and nitrate), whereas the correlation between WSOC and EC was much weaker, suggesting that WSOC should be dominated by secondary species. Moreover, the trend of the WSOC to EC ratio was found to coincide with that of relative humidity during winter, spring and fall. High WSOC/EC ratio in February indicates high humidity could enhance the formation potential of WSOC in winter. Sources of WSOC were further investigated by a receptor model (Positive Matrix Factorization model). The apportionment results suggested that biomass burning contributed about 40% of WSOC while about 54% of WSOC was associated with oxalate and sulfate, whereas a primary factor was responsible for only 6% of WSOC also demonstrating that primary emissions are not the main source of WSOC. Ó 2014 Published by Elsevier Ltd.

Keywords: WSOC SOA Biomass burning Source apportionment

1. Introduction Organic aerosol plays an important role in visibility reduction, regional and global climate change, and is known for its adverse

* Corresponding authors. State Key Joint Laboratory of Environment Simulation and Pollution Control, Department of Environmental Science and Engineering, Tsinghua University, Beijing, China. E-mail addresses: [email protected] (K. He), [email protected]. edu.cn (Y. Cheng). http://dx.doi.org/10.1016/j.atmosenv.2014.04.060 1352-2310/Ó 2014 Published by Elsevier Ltd.

health effects. Organic aerosol comprises numerous individual organic compounds, whereas only a small fraction (of the order of 10%) can be identified at a molecular level (Hallquist et al., 2009), and thus, the understanding of its sources and properties is still limited. At present, one challenge in atmospheric science is the large discrepancies between measured and modeled concentrations of secondary organic aerosol (SOA) (Heald et al., 2005; Vutukuru et al., 2006). Previous studies suggested that water-soluble organic carbon (WSOC) is mainly associated with SOA formation. Correlated

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diurnal variations between WSOC to Organic Carbon (OC) ratio and O3 in summer presumably indicated that a significant fraction of WSOC was from secondary formation (Miyazaki et al., 2007; Sullivan et al., 2004). Studies have also shown that WSOC strongly correlated with oxygenated organic aerosols (OOA), which are widely used as an estimate of SOA (de Gouw et al., 2009; Kondo et al., 2007; Xiao et al., 2011). In addition, WSOC values agreed well with SOA concentrations estimated by a number of methods, including the elemental carbon (EC)-tracer method (Cheng et al., 2011; Miyazaki et al., 2006), and the chemical mass balance (CMB) model (Snyder et al., 2009; Stone et al., 2008). The correlation between WSOC and secondary species such as inorganic ions and oxalate provides additional evidence for the secondary nature of WSOC (Cho and Park, 2013; Yang et al., 2005). Primary contributions of fossil fuel combustion to WSOC have also been thought to be considerable. However, a recent source emission study showed that light-duty gasoline vehicles and heavy-duty diesel vehicles emitted little primary particles (Gordon et al., 2013), while parallel measurements by Weber et al. (2007) showed that WSOC level at a roadside site was almost the same with a nearby urban site. A source apportionment study conducted in the southeastern United States found that only 13% of WSOC was attributed to mobile sources (Hecobian et al., 2010). Therefore, direct emissions may not be important to the ambient WSOC abundance as previously thought. Moreover, aircraft-based measurements found no increase of WSOC in the plumes from coalfired power plants in Atlanta compared to background aerosols (Peltier et al., 2007), indicating that coal burning may not be an important primary source of WSOC either. Ambient WSOC concentrations have been observed to be significantly influenced by biomass burning (Viana et al., 2008; Wonaschutz et al., 2011). However, the formation mechanisms of biomass burning-related WSOC are still not well understood. The solubility of organic aerosol in biomass burning plumes could be increased by both the formation of SOA (Kawamura et al., 2013) and the aging processes (Timonen et al., 2013), thus, the increase of WSOC levels when impacted by biomass burning does not necessarily mean that the observed WSOC is primary. A chamber study suggested that a significant fraction of semi-volatile organic carbon (SVOC) in primary particles would evaporate with increasing dilution and then serve as precursors for SOA formation (Robinson et al., 2007), indicating WSOC measured in source emission samples might not be completely primary. Field observations also suggest that a major fraction of the WSOC is from secondary sources (de Gouw et al., 2009; Kondo et al., 2007; Miyazaki et al., 2006), and WSOC is frequently used as a proxy for SOA in many studies (Kondo et al., 2007; Rengarajan et al., 2011; Zhang et al., 2012). WSOC can also influence the formation of cloud condensation nuclei (CCN), which exert important effects on climate (Asa-Awuku et al., 2009). Besides, WSOC is also linked to possible health effects; probes that measure the oxidative potential, or ROS (Reactive Oxygen Species) content of aerosols (e.g., dithiothreitol (DTT) assay) are often correlated with WSOC (Verma et al., 2012). Because of its importance, WSOC has been frequently measured worldwide, and previous studies found that it usually accounts for a substantial fraction of organic carbon (20e80%) (Feng et al., 2006; Jaffrezo et al., 2005; Park and Cho, 2011). Biomass burning contributed to about 40% of the national emissions of black carbon in China (Lu et al., 2011), and the ambient levels of biomass burning tracer (levoglucosan) were observed to be high throughout the year in Chinese cities (Zhang et al., 2008). Therefore, China is a unique location for evaluating WSOC as an indicator for SOA under large contributions of biomass burning. This paper is the first in a series of two papers aimed at investigating the sources and secondary

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nature of WSOC in Beijing, whereas the second paper focuses on the light absorption properties of WSOC (Du et al., 2014). This paper presents a near continuous thirteen month record of the chemical composition of fine particulate matter (including levoglucosan as well as other organic and inorganic components) collected in Beijing, China. To investigate the primary vs. secondary nature of WSOC, WSOC is compared with secondary inorganic components (e.g., sulfate), SOA mass predicted by the EC-tracer method, and meteorological factors (e.g., relative humidity). A receptor model is employed for the source apportionment of WSOC. 2. Methods 2.1. Sampling Ambient 24-h integrated PM2.5 samples were collected with a high volume (Hi-Vol) sampler (Thermo Scientific, MA, USA) on the Tsinghua University campus in Beijing, China. The sampling site is an urban site, located about 20 km northwest of the center of Beijing, and is surrounded by residential areas, with no major industrial sources around. The filter sampling started on 10 October 2010 and ended on 31 November 2011, and was interrupted for 16 days during the spring festival and occasionally due to instrument maintenance. A total of 399 samples were collected. Pre-baked Quartz filters (8  10 in, 2500 QAT-UP; Pall Corporation, NY, USA) were used for the sampling. After sampling, the filters were wrapped in aluminum foil and stored in freezer until analyses. The Hi-Vol samplers were operated at a flow rate of 1.13 m3/min and the exposed filter area was 406.5 cm2, leading to a face velocity of 0.46 m/s. Field blank filters were also collected every one or two weeks. These field blank filters were loaded into the sampler and removed after one minute without any air flow. Then they were stored and analyzed with other samples. The data presented in the study were blank-corrected. 2.2. Sample analysis The ambient filters were analyzed for OC/EC, WSOC, levoglucosan and water-soluble ions. In this study, limits of detections (LOD) were estimated based on blank variability (3s), and parallel analyses of both ambient filters and standards used for calibration were performed to determine the uncertainties of different species. The Sunset carbon analyzer (Sunset Laboratory Inc., OR, USA) was used for the OC/EC analysis. The IMPROVE protocol was implemented and the transmittance charring correction was used (the reflectance signal was not monitored by the Sunset analyzer). LOD and uncertainty of OC and EC were 0.1 mgC/m3 and below 0.01 mgC/ m3, and 7% and 10% respectively. For the analysis of water-soluble species, a punch with an area of 1.5 cm2 was taken from each sample and extracted with 60 ml of >18U Milli-Q water (Barnstead Nanopure system, Thermo Scientific, MA, USA) in amber nalgene HDPE bottles via 30-min sonication. The liquid extract was then filtered using a 0.45 mm PTFE syringe filter and transferred to another clean bottle for further analysis. WSOC was quantified using a Sievers Total Organic Carbon (TOC) Analyzer (Model 900; GE Analytical Instruments, CO, USA). The instrument was calibrated with a series of sucrose standards every week during the analysis period. Estimated LOD of WSOC was 0.1 mgC/m3 while the uncertainty was of 6%. Water-soluble ions were measured by a dual channel Dionex DX-500 Ion Chromatograph (Dionex Corporation, CA, USA). The Dionex AS11-HC anion column and the Dionex CS12A cation column were employed for the analysis of anions and cations, respectively. Calibration was performed every week using standards (7-Anion standard II, 6Cation standard II) purchased from Thermo Scientific. Uncertainties

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were 7% for Nitrate, 8% for sulfate and 24% for oxalate. LOD for the ions were about 0.1 mg/m3 for nitrate and sulfate, and 0.03 mg/m3 for oxalate. Levoglucosan was determined by high-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC-PAD) on a separate Dionex ion chromatography system (DX-500, Dionex Corporation, CA, USA). Levoglucosan was separated from other carbohydrates using a Dionex CarboPac MA1 column with a CarboPac MA1 guard column, which thoroughly separates monosaccharides. Detailed information on the eluent preparation, gradient and flow rate can be found in Iinuma et al. (2009). Calibrations for levoglucosan were conducted every week. Estimated LOD for levoglucosan was 20 ng/m3, while the uncertainty was 9%. 2.2.1. Meteorology parameters Meteorology data with a time resolution of 30 min were obtained from weather underground (www.wunderground.com), including relative humidity, temperature, wind speed and direction. The average meteorology data for each sample was calculated by averaging the raw data according to the start and stop time of each sampling event.

China, such as Shanghai and Guangzhou (Feng et al., 2006). Despite the differences in WSOC concentrations, similar seasonal variations of WSOC were observed in different regions. Although WSOC in spring and fall were rarely investigated, it has been reported that WSOC was usually higher in winter than summer, which was suspected to be associated with high biomass burning in winter (Jaffrezo et al., 2005; Park and Cho, 2011). 3.2. Secondary nature of WSOC In this study, strong correlation was also found between OC and WSOC (R2 > 0.9), and the contribution of WSOC to OC exhibited considerable temporal variations, as illustrated in Fig. 2. WSOC/OC was highest in summer (0.61 on average), and was moderately lower (about 0.4) during other seasons. The higher WSOC/OC ratios in summer compared with the other seasons have been found at a variety of locations, both urban and rural, worldwide, and was thought to be caused by the strong photochemical processes in summer (Jaffrezo et al., 2005; Kondo et al., 2007; Park and Cho, 2011; Sullivan et al., 2004). As shown in Table 2, poor correlation between WSOC and EC was found for most of the months (R2 < 0.5), also indicating that WSOC is more closely related to SOA. The secondary nature of WSOC is further discussed below.

3. Results and discussion 3.1. Temporal variations of WSOC Temporal variations of the identified species and the meteorology parameters are illustrated in Fig. 1. The seasonal average levels of the species are summarized in Table 1. The concentration of WSOC in fall of 2010 and 2011 was the highest among all the seasons (around 8.6 mgC/m3). The average concentration of WSOC in winter (8.0 mgC/m3) was slightly lower but close to that of fall. WSOC was lowest in spring (4.7 mgC/m3) and was around 6.7 mgC/ m3 in summer. WSOC concentrations in Beijing, either winter or summer, were higher than most other regions, such as Atlanta, USA (Sullivan et al., 2004), Barcelona, Spain (Viana et al., 2008) and Gwangju, Korea (Park and Cho, 2011). However, comparable or even higher WSOC concentrations were also found in other cities in

3.2.1. Correlation of WSOC and SOC estimated by the EC-tracer method In this study, concentrations of secondary organic carbon (SOC) were calculated separately for each month by the EC-tracer method:

SOC ¼ OC  ðOC=ECÞprim  EC  OCnoncomb where (OC/EC)prim is the ratio of OC to EC for primary emissions; OCnon-comb is primary OC emitted from non-combustion sources (biogenic and other sources). Slope and intercept of the linear regression of OC on EC for a subset of samples with lowest OC/EC ratio (or under highly unfavorable condition for SOC formation) have been suggested as an estimation of (OC/EC)prim and OCnoncomb, respectively. However, the minimum OC/EC ratio has also been used as (OC/EC)prim in various studies, when the time

Fig. 1. Temporal variations of meteorological conditions and identified chemical components over a 14-month period beginning October 2010.

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Table 1 Seasonal averaged concentrations of the identified species. Fall of 2010, winter of 2010, spring of 2011, summer of 2011, and fall of 2011 indicate Oct to Nov, 2010; Dec, 2010 to Feb, 2011; Mar to May, 2011; June to Aug, 2011; Sep to Nov, 2011, respectively. OC mgC/m3 Fall of 2010 Winter of 2010 Spring of 2011 Summer of 2011 Fall of 2011 Total

20.4 20.6 10.2 10.7 19.7 15.7

     

EC mgC/m3

15.4 16.1 6.8 6.2 15.4 13.2

8.6 7.0 4.9 3.0 4.2 5.2

     

6 3.6 3.2 1.4 1.7 3.7

WSOC mgC/m3 8.6 8.0 4.7 6.7 8.6 7.2

     

6.4 6.7 3.1 4.4 6.1 5.5

Levoglucosan ng/m3 675 354 154 150 522 342

     

494 277 131 228 455 383

Sulfate mg/m3 9.8 10.8 7.8 24.7 14.6 14.0

     

10.1 13.4 7.9 19.3 14.7 15.2

Nitrate mg/m3 15.2 12.3 11.4 17.7 18.4 15.1

     

15.4 18.4 14.2 16.2 18.5 16.8

Oxalate mg/m3 0.276 0.195 0.265 0.669 0.490 0.375

     

0.224 0.137 0.248 0.556 0.321 0.384

Fig. 2. Statistical results of WSOC/OC and OC/EC. Boxes represent the upper and lower quartiles. Whisker represent the maximum and minimum value. Cross and circle in the middle represent mean and median value, respectively.

Table 2 Correlation coefficient (R2) between species.

Oct-2010 Nov-2010 Dec-2010 Jan-2011 Feb-2011 Mar-2011 Apr-2011 May-2011 Jun-2011 Jul-2011 Aug-2011 Sep-2011 Oct-2011 Nov-2011

WSOC & OC

WSOC & levoglucosan

WSOC & EC

WSOC & sulfate

WSOC & nitrate

WSOC & SOA

0.95 0.98 0.94 0.96 0.94 0.92 0.92 0.94 0.98 0.97 0.96 0.99 0.91 0.97

0.77 0.92 0.95 0.96 0.94 0.88 0.64 0.51 0.70 0.58 0.51 0.86 0.81 0.95

0.20 0.37 0.56 0.73 0.34 0.60 0.56 0.32 0.63 0.52 0.49 0.14 0.22 0.18

0.73 0.82 0.80 0.89 0.89 0.87 0.81 0.93 0.42 0.65 0.35 0.78 0.77 0.64

0.71 0.85 0.95 0.91 0.89 0.88 0.90 0.69 0.22 0.71 0.37 0.85 0.82 0.73

0.86 0.87 0.67 0.90 0.91 0.84 0.66 0.80 0.95 0.88 0.87 0.97 0.90 0.97

resolution of OC and EC data was relatively low (Cheng et al., 2011; Rengarajan et al., 2011; Shakya et al., 2010). In this study, the minimum OC/EC was applied as (OC/EC)prim for each month. Meanwhile, since the contribution of non-combustion sources to OC was shown to be small in the urban area of Beijing (Lin et al., 2009), the OCnon-comb was assumed to be zero. Box plots of OC/EC for each month were shown in Fig. 2. The minimum OC/EC in each month usually varied between 0.9 and 1.6, but peaked at 2.3 in February. As shown in Fig. 1, all the samples in February were influenced by relatively high temperature (>0  C) and humidity (>50%) but low wind speed, which was favorable for the accumulation of primary pollutants and the formation of secondary components. As a result, the contribution of secondary species might be considerable in all of the samples collected in February, indicating that SOC might be considerably underestimated by the EC-tracer method in February.

As listed in Table 2, the correlation coefficients of WSOC and SOC were higher than 0.8 for all months except for April 2011 (R2 ¼ 0.66). The high correlation suggested the similar sources and formation mechanism for WSOC and SOC. In summer, photochemical production was thought to contribute significantly to WSOC (Miyazaki et al., 2007; Sullivan et al., 2004), which might lead to high correlation between SOC and WSOC in Beijing. However, in winter, when WSOC was more closely linked to biomass burning, the high correlation of WSOC and SOC also suggested that this WSOC acted more similarly to secondary species rather than species emitted directly from primary sources. Similar results were also reported during winter in Tokyo, where the correlation coefficient between WSOC and levoglucosan reached 0.93 (Kumagai et al., 2010) and half of the WSOC was estimated to be related to biomass burning (Minoura et al., 2012). However, other studies showed that WSOC in winter in Tokyo also exhibited high correlation with SOC estimated by the EC-tracer method (R2 ¼ 0.75, Miyazaki et al., 2006) and oxygenated organic aerosols (OOA) derived by the Aerodyne aerosol mass spectrometer (AMS) (R2 ¼ 0.86, Kondo et al., 2007). Results from these studies in Tokyo indicated that in spite of the strong association between WSOC and biomass burning in winter, the WSOC was still closely related to SOA. 3.2.2. Correlations of WSOC with secondary inorganic ions Correlations of WSOC with secondary inorganic ions (sulfate and nitrate) were also investigated (Table 2). It was found that WSOC correlated well with sulfate from November 2010 to May 2011 (R2 > 0.8), and the correlation coefficients were slightly lower in October 2010 and fall of 2011 but were still around 0.7. In contrast, in summer, WSOC and sulfate exhibit a relatively weaker correlation (0.3 < R2 < 0.65). The correlation between WSOC and nitrate in different seasons was similar with that of WSOC and sulfate. However, it was noteworthy that sampling artifacts for

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Fig. 3. Temporal variations of WSOC/EC and relative humidity (RH) in (a) January, (b) February, (c) April and (d) October of 2011.

nitrate might be significant in summer, which could lead to a low correlation between WSOC and nitrate. Moderate correlations between WSOC with sulfate (R2 ¼ 0.46 in 2008; R2 ¼ 0.69 in 2011) and nitrate (R2 ¼ 0.65 in 2008) in winter were observed in Gwangju, Korea (Cho and Park, 2013; Park and Cho, 2011). High correlation between WSOC with sulfate and nitrate (R2 ¼ 0.78 and 0.86, respectively) was reported in fall and winter in Nanjing, China (Yang et al., 2005). Correlation between WSOC and sulfate possibly indicates that they have similar sources or formation pathways However, inventory studies have shown that the main source of sulfur dioxide (precursor of sulfate) is coal combustion (Lu et al., 2011; Zhang et al., 2009), whereas the correlation between WSOC and levoglucosan in this study indicates that WSOC was closely related to biomass burning in fall and winter. Meanwhile, results in Peltier et al. (2007) have suggested that coal combustion is not likely an important source for WSOC. The major production pathway of sulfate has been suggested to be aqueous reactions. For example, a study conducted during winter in Hong Kong, China showed that the size distributions of sulfate peaked in the droplet mode, indicating the formation of sulfate was mainly due to in-cloud processes (Yao et al., 2002). Similar size distributions of sulfate were also found in Jinan, China (Wang et al., 2012). In this case, the strong correlation between WSOC and sulfate could indicate the importance of aqueous phase reactions for WSOC formation. In summer, other formation pathways such as photochemical reactions could also be important, and the correlation between WSOC and sulfate was weaker. This hypothesis is further explored by investigating the relationship between WSOC and relative humidity.

3.2.3. Relationship between WSOC and relative humidity Recent studies have suggested that organic aerosol could be formed in cloud and aerosol water (aqSOA), which is estimated to make a comparable contribution to the total aerosol loading as the SOA formed by vapor pressure-driven partitioning (gasSOA) (Ervens et al., 2011 and the references therein). In this study, WSOC/ EC was used to investigate the influence of humidity on the formation potential of WSOC, where EC was applied as an estimation of the strength of primary sources. Results from winter (January and February, 2011), spring (April, 2011) and fall (October, 2011) were shown in Fig. 3. In general, the variations of the WSOC/EC ratio coincided with that of relative humidity. Moreover, generally higher WSOC/EC ratios were found under higher humidity conditions, consistent with the hypothesis that WSOC formation may be linked with aqueous processes. However, the variations of WSOC/ EC and humidity were quite different in summer (R2 ¼ 0.06, Fig. 4), which may indicate the importance of photochemical processes in WSOC formation. It was also noteworthy that the average WSOC/EC was 2.6 in February, much higher than January (0.7), indicating stronger WSOC formation potential under high relative humidity. The tripled February WSOC/EC compared to January could ate that high humidity in winter could greatly enhance the production of WSOC. 3.2.4. Biomass burning and WSOC Concentration of levoglucosan, a tracer for biomass burning, was found highest in fall and winter, and much lower in spring and summer (Table 1), consistent with the seasonal variations of levoglucosan observed in other regions (Chen et al., 2013;

Fig. 4. Temporal variation of WSOC/EC and humidity in the summer of 2011.

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Fig. 5. Composition profiles (% of total of each species) resolved by the PMF analysis.

Kumagai et al., 2010; Puxbaum et al., 2007; Zhang et al., 2010). Levoglucosan was highly correlated with WSOC in fall and winter (R2 > 0.8, Table 2), indicating a strong association between WSOC and biomass burning. In spring and summer, moderate correlations were observed (R2 w0.6). Moreover, from 14 to 22 June 2011, a biomass burning episode was noticed, which was marked by high concentrations of levoglucosan comparable to that in winter. A significant increase of levoglucosan in summer was also found in previous studies, and was attributed to the burning of wheat residues in farmland after the harvest (Duan et al., 2004; Zheng et al., 2005). In addition, it is noteworthy that the concentrations of levoglucosan measured in Beijing were much higher than that of developed countries. For example, with the biomass burning episode excluded, high levoglucosan concentration was observed in the summer in Beijing (98.5  42.8 ng/ m3), compared with less than 30 ng/m3 in Europe (Puxbaum et al., 2007), 37.4 ng/m3 in the Kanto Plain, Japan (Kumagai et al., 2010) and 18.7 ng/m3 in the southeastern United States (Zhang et al., 2010). Therefore, the influences of biomass burning emissions on WSOC are expected to be important in Beijing, even in summer. However, as mentioned in Sections 3.2.1e3.2.3, all the evidence indicates that WSOC in the different seasons all showed a secondary nature.

3.3. Source apportionment of WSOC Source apportionment of WSOC was performed by the Positive Matrix Factorization (PMF) 3.0 model developed by the Environmental Protection Agency (EPA) of the United States. WSOC, levoglucosan, sulfate, oxalate, OC and EC were employed in the source apportionment. Light absorption of WSOC (Abs365) was also included in the PMF analysis but will be discussed separately in paper II of this series (Du et al., 2014). Data in February were recognized as an unusual period and was excluded from the PMF analysis. Thus a total of 371 samples were used for the source apportionment. When running the PMF model, the number of factors resolved has substantial influence on the apportionment result such that a low number of factors could not efficiently separate out different sources while too many factors would result in the split of one type of source into several unrealistic factors. In this study, the PMF solutions with different number of factors were examined. Factor profiles and time series together with the statistical parameters calculated by the PMF model were applied to determine the optimal solution. A result with four factors was selected as the optimal solution (Fig. 5). The Q (True) and Q (robust) values of this solution calculated by PMF agreed well, indicting no influence of peak events. Values reconstructed by PMF of each

Fig. 6. Source contributions to WSOC.

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species correlated strongly with input concentrations (R2 was between 0.90 and 0.99) with slops between 0.88 and 1.01. Results of bootstrap runs and G-Space plots showed the solution was stable and reliable. Factor 1 was characterized by the high levels of WSOC, Abs365 and levoglucosan, and was attributed to biomass burning. It was likely that factor 1 does not represent a typical type of primary source since only a relatively small fraction of EC was found in this factor. Factor 2 had a high loading of oxalate but negligible EC, indicating the characteristics of secondary organic aerosol. Aqueous phase reactions (or in-cloud processes) have been suggested to be the main pathway for oxalate formation (Huang et al., 2006; Yao et al., 2002), thus, factor 2 was likely associated with aqueous processes. It is also noted that 40.7% of WSOC was in factor 2, indicating aqueous phase reactions could be important for WSOC production in Beijing. Nearly 100% of sulfate (the formation of which is mainly due to aqueous phase reactions) was found in factor 3, while a substantial fraction (13.0%) of WSOC was also observed in this factor also demonstrating the importance of aqueous phase reactions for WSOC formation. Factor 4 was characterized by the high loading of EC indicating its relationship with mixed primary sources. Only a small fraction of WSOC (6%) was found in factor 4, providing more evidence for the conclusion that WSOC was mainly due to secondary formation. Contributions of the 4 factors to WSOC throughout the sampling period are shown in Fig. 6. The contribution of biomass burning to total WSOC was 39% annually, which was more significant in fall (w60%) and winter (w45%), but not negligible in spring (20%) and summer (11%). WSOC associated with oxalate and sulfate (factors 2 and 3) together accounted for 71% and 84% of total WSOC in spring and summer, respectively, and was also important in fall and winter (>40% of total WSOC). The contribution of WSOC attributed to the mixed primary factor (factor 4) only contributed 6e10% to the total WSOC in fall, winter and spring, and only accounted for 4% of the total WSOC in summer. 4. Conclusions PM2.5 samples were collected on Tsinghua University, Beijing during a campaign from October 2010 to November 2011 and were analyzed for chemical components to investigate the sources and primary vs. secondary nature of WSOC. WSOC averaged 7.2 mgC/m3 annually and correlated strongly with OC. The WSOC to OC ratio averaged 0.46 annually and was substantially higher in summer. WSOC exhibited strong correlations with secondary components such as SOA estimated by the EC-tracer method and inorganic ions (e.g., sulfate and nitrate). The correlation between WSOC and EC was much weaker, indicating WSOC might be dominated by secondary components. In addition, the high WSOC/EC ratio in February indicates high humidity could enhance the formation potential of WSOC in winter. Sources of WSOC were further investigated using factor analysis (i.e., PMF). The apportionment results suggested that the primary factor was responsible for only 6% of WSOC, which provided strong evidence that primary emissions are not the main source of WSOC. Biomass burning contributed about 40% of WSOC, consistent with the good correlation between WSOC and levoglucosan. In addition, the remaining WSOC (54%) was found to be associated with oxalate and sulfate, both of which are thought to be formed from aqueous phase reaction. Acknowledgements This work was supported by the National Natural Science Foundation of China (21307067, 21190054 and 21107061) and the

China Postdoctoral 2013M540104).

Science

Foundation

(2013T60130

and

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